ArticleGenetics2012
Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).
Article in Genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 189 papers.
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Who cites it
189 citing papers in PubMed.
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- Uncertainty-aware breeding decisions: MCMC-based optimum contribution selection increases breeding decision robustness.Genetics · 2026Article
- Genomic selection validated across two generations of loblolly pine breeding.G3 (Bethesda, Md.) · 2026Article
- Proximal regularization of deep residual neural networks applied to high-dimensional genomic data.Briefings in bioinformatics · 2026Article
- Article
- Breeding for climate adaptation: genetic variation and genomic selection for drought response in Scots pine.BMC genomics · 2026Article
- Enhancing genomic prediction accuracy in Huaxi cattle through integration of transcriptomic data and a self-attention-based SNP selection strategy.BMC bioinformatics · 2026Article
- Development and application of a genotyping by target sequencing single-nucleotide polymorphism array panel in Salix suchowensis.BMC genomics · 2026Article
- Genomic selection of root-knot nematode (Meloidogyne enterolobii) resistance in watermelon wild relatives (Citrullus amarus).The plant genome · 2026Article
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Crop phenotype prediction using SNP context and whole-genome feature embedding based on DNABERT-2.Plant methods · 2026Article
- Estimation of Genetic Parameters for Growth Traits and Genomic Selection Study in Siniperca Scherzeri.Marine biotechnology (New York, N.Y.) · 2026Article
- Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce.The plant genome · 2025Article
- Application and development prospect of genomic selection breeding in coniferous trees.Planta · 2025Review
- EasyGeSe - a resource for benchmarking genomic prediction methods.BMC genomics · 2025Article
- Deciphering the regulatory network of carbon isotope discrimination in bread wheat through genome-wide association studies and genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Cross-generational genomic prediction of Norway spruce (Picea abies) wood properties: an evaluation using independent validation.BMC genomics · 2025Article
- Multi-task genomic prediction using gated residual variable selection neural networks.BMC bioinformatics · 2025Article
- Genomic selection of maize test-cross hybrids leveraged by marker sampling.The plant genome · 2025Article
129 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression-best linear unbiased prediction (RR-BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR-BLUP (RR-BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR-BLUB B had higher predictive ability than RR-BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.
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